Technology landscape
How reported AI classifications spread across agencies, missions, lifecycle stages and sourcing. Each record carries one reported classification, so categories do not overlap. A classification describes the technique, not a specific commercial model.
Lifecycle by technology
Share of each technology's recordsSourcing by technology
Where reportedDeveloped in-house: 8 (18.2%)Generative AI
Contract and in-house: 22 (50.0%)Generative AI
Purchased from vendor: 14 (31.8%)
Developed in-house: 16 (34.8%)Classical / predictive ML
Contract and in-house: 22 (47.8%)Classical / predictive ML
Purchased from vendor: 8 (17.4%)
Developed in-house: 16 (29.1%)Natural language processing
Contract and in-house: 30 (54.5%)Natural language processing
Purchased from vendor: 9 (16.4%)
Technology adoption by agency
Row share: each agency's records by classification · 16 largest portfolios in the filterTechnology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Other | n | |
|---|---|---|---|---|---|---|---|
| Administrative Functions | 31% | 29% | 33% | 4% | 2% | 51 | |
| Health and Medical | 13% | 35% | 46% | 4% | 2% | 46 | |
| Science | 13% | 30% | 13% | 39% | 4% | 23 | |
| Information Technology | 50% | 11% | 33% | 6% | 18 | ||
| Service Delivery | 22% | 33% | 44% | 9 | |||
| Other | 40% | 20% | 20% | 20% | 5 | ||
| Procurement and Financial Management | 60% | 20% | 20% | 5 | |||
| Emergency Management | 50% | 50% | 4 | ||||
| Human Resources | 100% | 3 | |||||
| Government Benefits Processing | 50% | 50% | 2 | ||||
| Law Enforcement | 100% | 1 |
High-impact designation by technology
Presumed high-impact, determined not: 5 (10.9%)Classical / predictive ML
Not high-impact: 41 (89.1%)
Presumed high-impact, determined not: 2 (15.4%)Computer vision
Not high-impact: 11 (84.6%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 0 | – | – | – | – | – |
| Classical / predictive ML | 0 | – | – | – | – | – |
| Natural language processing | 0 | – | – | – | – | – |
| Computer vision | 0 | – | – | – | – | – |
| Agentic AI | 0 | – | – | – | – | – |
| Other | 0 | – | – | – | – | – |
Shows counts reported in place against all high-impact records of that technology. The remainder includes in-progress, not applicable and unanswered. Full governance view
Systems, vendors and technology are different things
100 of 167 records name a vendor (60%)Vendor: the supplier the agency names in a free-text field. Matched to a standard list of companies; one record may name several.
System name: the agency's name for the system (reported for 75% of records).
AI classification: the technique category the agency selected. A vendor name does not show which model or technique is used, and this platform does not infer it.